Description Usage Arguments General Methods Author(s) References See Also
Plot results from fitted mixture of 2-process Poisson models, and calculate the bout ending criterion.
1 2 3 4 5 6 7 8 9 10 |
fit |
|
x |
numeric object with variable modelled. |
... |
Arguments passed to the underlying
|
signature(fit="nls")
: Plot fitted 2- or
3-process model of log frequency vs the interval mid points,
including observed data.
signature(x="mle")
: As the nls
method, but models fitted through maximum likelihood method. This
plots the fitted model and a density plot of observed data.
signature(fit="nls")
: Extract the estimated bout
ending criterion from a fitted 2-process model.
signature(fit="mle")
: As the nls method, but
extracts the value from a maximum likelihood model.
signature(fit="nls")
: Extract the estimated bout
ending criterion from a fitted 3-process model.
Sebastian P. Luque spluque@gmail.com
Berdoy, M. (1993) Defining bouts of behaviour: a three-process model. Animal Behaviour 46, 387-396.
Langton, S.; Collett, D. and Sibly, R. (1995) Splitting behaviour into bouts; a maximum likelihood approach. Behaviour 132, 9-10.
Luque, S. P. and Guinet, C. (2007) A maximum likelihood approach for identifying dive bouts improves accuracy, precision, and objectivity. Behaviour 144, 1315-1332.
Mori, Y.; Yoda, K. and Sato, K. (2001) Defining dive bouts using a sequential differences analysis. Behaviour 138, 1451-1466.
Sibly, R.; Nott, H. and Fletcher, D. (1990) Splitting behaviour into bouts. Animal Behaviour 39, 63-69.
bouts.mle
, bouts2.nls
,
bouts3.nls
for examples.
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